Chronic Low Back Pain
Bibliographic record
Abstract
"Chronic" low back pain (LBP), defined as present for 3 or more months, has become a major socioeconomic problem insufficiently addressed by five major entities largely working in isolation from one another - procedural based specialties, strength based rehabilitation, cognitive behavioral therapy, pain management and manipulative care. As direct and indirect costs continue to rise, many authors have systematically evaluated the body of evidence in an effort to demonstrate the effectiveness (or lack thereof) for various diagnostic and therapeutic interventions. The objective of this Spine Focus issue is not to replicate previous work in this area. Rather, our expert panel has chosen a set of potentially controversial topics for more in-depth study and discussion. A recurring theme is that chronic LBP is a heterogeneous condition, and this affects the way it is diagnosed, classified, treated, and studied. The efficacy of some treatments may be appreciated only through a better understanding of heterogeneity of treatment effects (i.e., identification of clinically relevant subgroups with differing responses to the same treatment). Current clinical guidelines and payer policies for LBP are systematically compared for consistency and quality. Novel approaches for data gathering, such as national spine registries, may offer a preferable approach to gain meaningful data and direct us towards a "results-based medicine." This approach would require more high-quality studies, more consistent recording for various phenotypes and exploration of studies on genetic epidemiologic undertones to guide us in the emerging era of "results based medicine."
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".